The advantages of interactive dialogue or tutoring as a teaching and learning technique in certain circumstances are well-known. Interactive dialogue allows the tutor to detect and remediate failed communications, incorrect student knowledge, and apparent gaps in the student's knowledge. Additionally, tutoring demands the student's attention in order to interact with the tutor, whereas the student's attention is more likely to wander when reading by himself or herself.
Recently, automated computer tutors, also known as Intelligent Tutoring Systems (ITS), have been developed that assist students in learning about a variety of topics, including, but not limited to, science, mathematics, technology, and computer literacy (e.g., hardware, software, and programming). An example of such a tutor program is AutoTutor, which presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard or by speech, formulates dialog moves that are sensitive to the learner's contributions (such as prompts, elaborations, corrections, and hints), and delivers the dialog moves with a talking head. The talking head serves as a conversation partner with the learner. It delivers AutoTutor's dialog moves with synthesized speech, appropriate intonation, facial expressions, and gestures. At the top of the computer interaction screen, AutoTutor prints the questions and problems that are produced from a curriculum script. These questions and problems invite lengthy responses and deep reasoning (e.g., answers to why, how, what-if), as opposed to being fill-in-the blank questions or shallow questions. There is a multi-turn tutorial dialog between AutoTutor and the learner during the course of answering a question (or solving a problem). The learner types in his/her contributions during the exchange by keyboard. For some topics, there are graphical displays and animation, with components that AutoTutor points to.
AutoTutor can keep the dialogue on track because it is constantly comparing students' contributions to expected answers. Sophisticated pattern matching and natural language processing mechanisms drive the comparisons, with the focus always being on the student's verbal contributions. Up to the present, AutoTutor and other ITS have been unable to detect and respond to emotional and non-verbal cues from the student. Verbal and non-verbal channels show a remarkable degree of sophisticated coordination in human-human communication. While the linguistic channel mainly conveys the content of the message, non-verbal behaviors play a fundamental role in expressing the affective states, attitudes, and social dynamics of the communicators. Although ubiquitous to human-human interactions, the information expressed through non-verbal communicative channels is largely ignored in human-computer interactions. Simply put, there is a great divide between the highly expressive human and the computer.